multiview radial basis function: a new approach on nonlinear forecasting of chaotic dynamic systems

Department: Electrical & Computer Engineering
Faculty Advisor(s): Shankar Subramaniam | Pamela C. Cosman

Primary Student
Name: Maryam Masnadi-Shirazi
Phone: 858-822-0986
Grad Year: 2018

The curse of dimensionality has long been a hurdle in the analysis of complex data in areas such as computational biology, ecology, econometrics and etc. In this work, we present a forecasting algorithm that exploits the dimensionality of data in a nonlinear autoregressive framework. The main idea is that the dynamics of a chaotic dynamical system consisting of multiple time-series can be reconstructed using a combinations of multiple variables. This nonlinear autoregressive algorithm uses attractors reconstructed from a combination of variables as the inputs of a neural network to predict the future. We show that our approach, multiview radial basis function (MV-RBF) provides better forecast skill than that of a model-free approach, multiview embedding (MVE), for simulated ecosystems and a mesocosm experiment. By taking advantage of dimensionality, we show that MV-RBF overcomes the shortcomings of noisy and short time-series.

Industry Application Area(s)
Control Systems | Internet, Networking, Systems

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